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ClearML demo

opdracht:

  • setup HPC clearml agent
  • train mnist model and optimize accuracy using agent
    • Log metrics in experiment -> run in clearml using agent
  • Deploy model so pictures from mnist can be send to API and prediction comes back
  • create pipeline that trains and deploys model on set timer (once a week)

Setup ClearML:

Setup ClearML Agent:

  • pip install clearml-agent
  • clearml-agent init
  • Create workspace credentials
    • Settings -> Workspace -> Create new credentials
    • copy code in clearml-agent init prompt in your terminal
    • enter for app, api and file host
    • Put in Git credentials
  • clearml-agent daemon --services-mode --queue test --create-queue
    • if run from same device as server, update clearml.conf with agent

serving

  • pip install clearml-serving
  • clearml-serving create --name "serving example"
    • Write down the Serving Service UID
  • git clone https://github.com/allegroai/clearml-serving.git
  • cat docker/example.env
  • cd docker && docker-compose --env-file example.env -f docker-compose-triton.yml up
  • clearml-serving --id <service_id> model add --engine triton --endpoint "test_model_pytorch" --preprocess "preprocess.py" --name "stage_train - serving_model" --project "pipeline" --input-size 1 28 28 --input-name "INPUT__0" --input-type float32 --output-size -1 10 --output-name "OUTPUT__0" --output-type float32
  • curl -X POST "http://127.0.0.1:8080/serve/test_model_pytorch" -H "Content-Type: image/png" --data-binary "data/mnist_png/0/3.jpg"

Server details: